An imputation method for categorical variables with application to nonlinear principal component analysis

Computational Statistics and Data Analysis

Volumn 55, Issue 7, 2011, Pages 2410-2420

  Ferrari, Pier Alda ^a   Annoni, Paola ^b   Barbiero, Alessandro ^a   Manzi, Giancarlo ^a  

^a UNIVERSITY OF MILAN   (Italy)

^b JOINT RESEARCH CENTRE   (Italy)

Author keywords

Composite indicators; Forward imputation; Imputation procedure; Listwise deletion; Nearest neighbor; Ordinal data; Passive treatment

Indexed keywords

COMPOSITE INDICATORS; FORWARD IMPUTATION; IMPUTATION PROCEDURE; LISTWISE DELETION; NEAREST NEIGHBORS; ORDINAL DATA; PASSIVE TREATMENT;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79953655977     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2011.02.007     Document Type: Article

References (23)

1. J.R. Carpenter, M.G. Kenward, and S. Vansteelandt A comparison of multiple imputation and doubly robust estimation for analyses with missing data Journal of the Royal Statistical Society: Series A 169 2006 571 584 (Pubitemid 43877183)
2. G. Chen, and T. stebro How to deal with missing categorical data: test of a simple Bayesian method Organizational Research Methods 6 3 2003 309 327
3. J.C. Deville, and Y. Till Unequal probability sampling without replacement through a splitting method Biometrika 85 1998 89 101
4. P.A. Ferrari, P. Annoni, and G. Manzi Evaluation and comparison of European countries: public opinion on services Quality and Quantity: International Journal of Methodology 2009 10.1007/s11135-009-9278-z
5. P.A. Ferrari, P. Annoni, and S. Urbisci A proposal for setting up vulnerability indicators in the presence of missing data Statistica & Applicazioni 4 1 2006 73 88
6. A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin Bayesian Data Analysis 1995 Chapman & Hall New York
7. A. Gifi Nonlinear Multivariate Analysis 1990 Wiley New York
8. R. Hron, M. Templ, and P. Filzmoser Imputation of missing values for compositional data using classical and robust methods Computational Statistics & Data Analysis 2009 10.1016/j.csda.2009.11.023
9. H. Jung, J.L. Schafer, and B. Seo A latent class selection model for nonignorably missing data Computational Statistics & Data Analysis 2010 10.1016/j.csda.2010.07.002
10. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data 2nd ed. 2002 John Wiley & Sons, Inc.
11. G. Michailidis, and J. de Leeuw The Gifi system of descriptive multivariate analysis Statistical Science 13 4 1998 307 336 (Pubitemid 128465328)
12. E.B. Page Ordered hypotheses for multiple treatments: a significance test for linear ranks Journal of the American Statistical Association 58 301 1963 216 230
13. G.P. Patil, and C. Taille Multiple indicators, partially ordered sets and linear extensions: multi-criterion ranking and prioritization Environmental and Ecological Statistics 11 2004 199 228 (Pubitemid 38865595)
14. C. Paul, W.M. Mason, D. McCaffrey, and S.A. Fox A cautionary case study of approaches to the treatment of missing data Statistical Methods and Applications 17 2008 351 372
15. D.B. Rubin Inference and missing data Biometrika 63 1976 581 592
16. S. Serneels, and T. Verdonck Principal component regression for data containing outliers and missing elements Computational Statistics & Data Analysis 53 2009 3855 3863
17. J. Siddique, and T.R. Belin Using an approximate Bayesian bootstrap to multiply impute nonignorable missing data Computational Statistics & Data Analysis 53 2008 405 415
18. M.S. Srivastava Methods of Multivariate Statistics 2002 John Wiley & Sons, Inc.
19. O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R.B. Altman Missing value estimation methods for DNA microarrays Bioinformatics 17 6 2001 520 525 (Pubitemid 32600498)
20. B. Walczak, and D.L. Massart Dealing with missing data: part I Chemometrics and Intelligent Laboratory Systems 58 2001 15 27 (Pubitemid 32824186)
21. B. Walczak, and D.L. Massart Dealing with missing data: part II Chemometrics and Intelligent Laboratory Systems 58 2001 29 42 (Pubitemid 32824187)
22. I. Wasito, and B. Mirkin Nearest neighbour approach in the least-squares data imputation algorithms Information Sciences 169 1 2005 1 25
23. I.R. White, R. Daniel, and P. Royston Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables Computational Statistics & Data Analysis 54 2010 2267 2275